Overview

Dataset statistics

Number of variables 24
Number of observations 91646
Missing cells 222970
Missing cells (%) 10.1%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 16.8 MiB
Average record size in memory 192.0 B

Variable types

Numeric 6
Text 9
Unsupported 2
Categorical 7

Alerts

aff_code has constant value "" Constant
page_channel has constant value "" Constant
fuel_type is highly imbalanced (79.9%) Imbalance
msrp has 24089 (26.3%) missing values Missing
local_zone has 91646 (100.0%) missing values Missing
interior_color has 4872 (5.3%) missing values Missing
price_badge has 91646 (100.0%) missing values Missing
trim has 1592 (1.7%) missing values Missing
dealer_name has 941 (1.0%) missing values Missing
dealer_zip has 941 (1.0%) missing values Missing
mileage has 2366 (2.6%) missing values Missing
cat has 1301 (1.4%) missing values Missing
exterior_color has 1042 (1.1%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
local_zone is an unsupported type, check if it needs cleaning or further analysis Unsupported
price_badge is an unsupported type, check if it needs cleaning or further analysis Unsupported
msrp has 19897 (21.7%) zeros Zeros
mileage has 3630 (4.0%) zeros Zeros

Reproduction

Analysis started 2024-05-20 04:58:36.607720
Analysis finished 2024-05-20 04:58:48.458928
Duration 11.85 seconds
Software version ydata-profiling vv4.7.0
Download configuration config.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct 91646
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 45822.5
Minimum 0
Maximum 91645
Zeros 1
Zeros (%) < 0.1%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:48.589615 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 4582.25
Q1 22911.25
median 45822.5
Q3 68733.75
95-th percentile 87062.75
Maximum 91645
Range 91645
Interquartile range (IQR) 45822.5

Descriptive statistics

Standard deviation 26456.066
Coefficient of variation (CV) 0.57735972
Kurtosis -1.2
Mean 45822.5
Median Absolute Deviation (MAD) 22911.5
Skewness 0
Sum 4.1994488 × 109
Variance 6.9992341 × 108
Monotonicity Strictly increasing
2024-05-19T23:58:48.767983 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 1
 
< 0.1%
61083 1
 
< 0.1%
61103 1
 
< 0.1%
61102 1
 
< 0.1%
61101 1
 
< 0.1%
61100 1
 
< 0.1%
61099 1
 
< 0.1%
61098 1
 
< 0.1%
61097 1
 
< 0.1%
61096 1
 
< 0.1%
Other values (91636) 91636
> 99.9%
Value Count Frequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
Value Count Frequency (%)
91645 1
< 0.1%
91644 1
< 0.1%
91643 1
< 0.1%
91642 1
< 0.1%
91641 1
< 0.1%
91640 1
< 0.1%
91639 1
< 0.1%
91638 1
< 0.1%
91637 1
< 0.1%
91636 1
< 0.1%

msrp
Real number (ℝ)

MISSING  ZEROS 

Distinct 10319
Distinct (%) 15.3%
Missing 24089
Missing (%) 26.3%
Infinite 0
Infinite (%) 0.0%
Mean 36288.707
Minimum 0
Maximum 329486
Zeros 19897
Zeros (%) 21.7%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:48.938811 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 35800
Q3 54315
95-th percentile 87525
Maximum 329486
Range 329486
Interquartile range (IQR) 54315

Descriptive statistics

Standard deviation 32017.981
Coefficient of variation (CV) 0.88231254
Kurtosis 5.3755323
Mean 36288.707
Median Absolute Deviation (MAD) 21735
Skewness 1.3289265
Sum 2.4515562 × 109
Variance 1.0251511 × 109
Monotonicity Not monotonic
2024-05-19T23:58:49.114901 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 19897
21.7%
46465 201
 
0.2%
44115 192
 
0.2%
35975 174
 
0.2%
54595 150
 
0.2%
34085 124
 
0.1%
36926 118
 
0.1%
32189 112
 
0.1%
33160 109
 
0.1%
26980 107
 
0.1%
Other values (10309) 46373
50.6%
(Missing) 24089
26.3%
Value Count Frequency (%)
0 19897
21.7%
5895 1
 
< 0.1%
5991 1
 
< 0.1%
5995 1
 
< 0.1%
6000 3
 
< 0.1%
6188 2
 
< 0.1%
6495 2
 
< 0.1%
6995 1
 
< 0.1%
7000 2
 
< 0.1%
7705 2
 
< 0.1%
Value Count Frequency (%)
329486 4
 
< 0.1%
326445 1
 
< 0.1%
317486 2
 
< 0.1%
311895 3
 
< 0.1%
309695 1
 
< 0.1%
300386 1
 
< 0.1%
298875 11
< 0.1%
295895 4
 
< 0.1%
295886 1
 
< 0.1%
288300 2
 
< 0.1%

year
Real number (ℝ)

Distinct 65
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 2020.1608
Minimum 1936
Maximum 2025
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:49.304745 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 1936
5-th percentile 2007
Q1 2019
median 2023
Q3 2024
95-th percentile 2024
Maximum 2025
Range 89
Interquartile range (IQR) 5

Descriptive statistics

Standard deviation 6.5060897
Coefficient of variation (CV) 0.0032205802
Kurtosis 18.433617
Mean 2020.1608
Median Absolute Deviation (MAD) 1
Skewness -3.3254881
Sum 1.8513966 × 108
Variance 42.329204
Monotonicity Not monotonic
2024-05-19T23:58:49.563132 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
2024 43324
47.3%
2023 6765
 
7.4%
2021 6658
 
7.3%
2020 4157
 
4.5%
2022 3766
 
4.1%
2019 3289
 
3.6%
2018 3109
 
3.4%
2017 2733
 
3.0%
2016 2432
 
2.7%
2015 2059
 
2.2%
Other values (55) 13354
 
14.6%
Value Count Frequency (%)
1936 7
 
< 0.1%
1953 1
 
< 0.1%
1957 36
< 0.1%
1959 9
 
< 0.1%
1960 15
< 0.1%
1961 4
 
< 0.1%
1964 8
 
< 0.1%
1965 25
< 0.1%
1966 7
 
< 0.1%
1967 5
 
< 0.1%
Value Count Frequency (%)
2025 855
 
0.9%
2024 43324
47.3%
2023 6765
 
7.4%
2022 3766
 
4.1%
2021 6658
 
7.3%
2020 4157
 
4.5%
2019 3289
 
3.6%
2018 3109
 
3.4%
2017 2733
 
3.0%
2016 2432
 
2.7%
Distinct 10316
Distinct (%) 11.3%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:50.045657 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 102
Median length 91
Mean length 28.129891
Min length 12

Characters and Unicode

Total characters 2577992
Distinct characters 80
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1682 ?
Unique (%) 1.8%

Sample

1st row Chevrolet:Blazer EV:RS:2024
2nd row RAM:ProMaster 2500:High Roof:2024
3rd row Mercedes-Benz:Sprinter 2500:High Roof:2024
4th row Honda:CR-V:EX:2024
5th row Chevrolet:Equinox:LS:2024
Value Count Frequency (%)
jeep:grand 2198
 
1.2%
se:2024 1645
 
0.9%
s 1542
 
0.9%
premium 1487
 
0.8%
mercedes-benz:amg 1320
 
0.7%
4matic:2024 1294
 
0.7%
chevrolet:silverado 1253
 
0.7%
volkswagen:tiguan:2.0t 1180
 
0.7%
ram:promaster 1165
 
0.7%
package:2024 1151
 
0.7%
Other values (9838) 162826
92.0%
2024-05-19T23:58:50.532919 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 2577992
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 2577992
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 2577992
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

model
Text

Distinct 1042
Distinct (%) 1.1%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:51.025416 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 28
Median length 24
Mean length 7.2026384
Min length 1

Characters and Unicode

Total characters 660093
Distinct characters 71
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 7 ?
Unique (%) < 0.1%

Sample

1st row Blazer EV
2nd row ProMaster 2500
3rd row Sprinter 2500
4th row CR-V
5th row Equinox
Value Count Frequency (%)
1500 2858
 
2.3%
grand 2399
 
1.9%
cherokee 2291
 
1.8%
2500 2233
 
1.8%
escape 1559
 
1.3%
outback 1538
 
1.2%
hybrid 1419
 
1.1%
tucson 1353
 
1.1%
amg 1346
 
1.1%
l 1324
 
1.1%
Other values (852) 105719
85.2%
2024-05-19T23:58:51.680778 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 660093
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 660093
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 660093
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

local_zone
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing 91646
Missing (%) 100.0%
Memory size 716.1 KiB

interior_color
Text

MISSING 

Distinct 1456
Distinct (%) 1.7%
Missing 4872
Missing (%) 5.3%
Memory size 716.1 KiB
2024-05-19T23:58:51.993341 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 62
Median length 5
Mean length 7.5585083
Min length 1

Characters and Unicode

Total characters 655882
Distinct characters 46
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 241 ?
Unique (%) 0.3%

Sample

1st row black
2nd row black
3rd row gray
4th row medium_ash_gray
5th row pearl_beige
Value Count Frequency (%)
black 34971
40.3%
gray 6214
 
7.2%
jet_black 5533
 
6.4%
ebony 4659
 
5.4%
charcoal 3771
 
4.3%
beige 1583
 
1.8%
global_black 1440
 
1.7%
titan_black 1147
 
1.3%
graphite 1036
 
1.2%
tan 969
 
1.1%
Other values (1445) 25451
29.3%
2024-05-19T23:58:52.564453 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 655882
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 655882
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 655882
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

aff_code
Categorical

CONSTANT 

Distinct 1
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
national
91646 

Length

Max length 8
Median length 8
Mean length 8
Min length 8

Characters and Unicode

Total characters 733168
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row national
2nd row national
3rd row national
4th row national
5th row national

Common Values

Value Count Frequency (%)
national 91646
100.0%

Length

2024-05-19T23:58:52.708720 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:52.847595 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
national 91646
100.0%

Most occurring characters

Value Count Frequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

price
Real number (ℝ)

Distinct 20233
Distinct (%) 22.3%
Missing 868
Missing (%) 0.9%
Infinite 0
Infinite (%) 0.0%
Mean 42623.437
Minimum 0
Maximum 1699800
Zeros 34
Zeros (%) < 0.1%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:53.023236 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 9995
Q1 23712
median 34298.5
Q3 51465
95-th percentile 93000
Maximum 1699800
Range 1699800
Interquartile range (IQR) 27753

Descriptive statistics

Standard deviation 42024.853
Coefficient of variation (CV) 0.98595646
Kurtosis 385.96192
Mean 42623.437
Median Absolute Deviation (MAD) 13318.5
Skewness 12.822971
Sum 3.8692703 × 109
Variance 1.7660882 × 109
Monotonicity Not monotonic
2024-05-19T23:58:53.209539 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
9995 271
 
0.3%
14995 248
 
0.3%
18995 234
 
0.3%
19995 229
 
0.2%
16995 223
 
0.2%
12995 221
 
0.2%
15995 211
 
0.2%
17995 207
 
0.2%
13995 206
 
0.2%
51465 199
 
0.2%
Other values (20223) 88529
96.6%
(Missing) 868
 
0.9%
Value Count Frequency (%)
0 34
< 0.1%
32 1
 
< 0.1%
1500 8
 
< 0.1%
1700 2
 
< 0.1%
1900 2
 
< 0.1%
1995 3
 
< 0.1%
2000 12
 
< 0.1%
2500 5
 
< 0.1%
2550 1
 
< 0.1%
2700 1
 
< 0.1%
Value Count Frequency (%)
1699800 13
< 0.1%
829800 8
< 0.1%
709800 13
< 0.1%
639800 2
 
< 0.1%
599800 8
< 0.1%
599000 1
 
< 0.1%
579999 2
 
< 0.1%
575800 1
 
< 0.1%
569900 2
 
< 0.1%
569895 2
 
< 0.1%

price_badge
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing 91646
Missing (%) 100.0%
Memory size 716.1 KiB

trim
Text

MISSING 

Distinct 2173
Distinct (%) 2.4%
Missing 1592
Missing (%) 1.7%
Memory size 716.1 KiB
2024-05-19T23:58:53.643703 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 75
Median length 66
Mean length 7.7482399
Min length 1

Characters and Unicode

Total characters 697760
Distinct characters 79
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 240 ?
Unique (%) 0.3%

Sample

1st row RS
2nd row High Roof
3rd row High Roof
4th row EX
5th row LS
Value Count Frequency (%)
base 9266
 
6.5%
premium 5733
 
4.0%
s 4647
 
3.3%
se 4486
 
3.2%
limited 4377
 
3.1%
sel 3536
 
2.5%
4matic 3523
 
2.5%
sport 3091
 
2.2%
2.0t 2619
 
1.8%
plus 2284
 
1.6%
Other values (1221) 98094
69.2%
2024-05-19T23:58:54.267960 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 697760
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 697760
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 697760
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

drivetrain
Categorical

Distinct 14
Distinct (%) < 0.1%
Missing 493
Missing (%) 0.5%
Memory size 716.1 KiB
All-wheel Drive
43862 
Front-wheel Drive
18652 
Four-wheel Drive
14890 
Rear-wheel Drive
10511 
AWD
 
1882
Other values (9)
 
1356

Length

Max length 58
Median length 17
Mean length 15.272684
Min length 3

Characters and Unicode

Total characters 1392151
Distinct characters 32
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) < 0.1%

Sample

1st row All-wheel Drive
2nd row Front-wheel Drive
3rd row Rear-wheel Drive
4th row Front-wheel Drive
5th row Front-wheel Drive

Common Values

Value Count Frequency (%)
All-wheel Drive 43862
47.9%
Front-wheel Drive 18652
20.4%
Four-wheel Drive 14890
 
16.2%
Rear-wheel Drive 10511
 
11.5%
AWD 1882
 
2.1%
FWD 614
 
0.7%
4WD 341
 
0.4%
Unknown 225
 
0.2%
RWD 169
 
0.2%
4x2 2
 
< 0.1%
Other values (4) 5
 
< 0.1%
(Missing) 493
 
0.5%

Length

2024-05-19T23:58:54.437124 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
drive 87920
49.1%
all-wheel 43864
24.5%
front-wheel 18652
 
10.4%
four-wheel 14890
 
8.3%
rear-wheel 10511
 
5.9%
awd 1882
 
1.1%
fwd 614
 
0.3%
4wd 341
 
0.2%
unknown 225
 
0.1%
rwd 169
 
0.1%
Other values (9) 13
 
< 0.1%

Most occurring characters

Value Count Frequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 1392151
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1392151
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1392151
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

dealer_name
Text

MISSING 

Distinct 597
Distinct (%) 0.7%
Missing 941
Missing (%) 1.0%
Memory size 716.1 KiB
2024-05-19T23:58:54.857923 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 86
Median length 54
Mean length 24.058817
Min length 6

Characters and Unicode

Total characters 2182255
Distinct characters 67
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 116 ?
Unique (%) 0.1%

Sample

1st row Castle Rock Chevrolet GMC
2nd row New Smyrna Chrysler Jeep Dodge RAM
3rd row Mercedes-Benz of Farmington
4th row Kingman Honda
5th row McSweeney Chevrolet GMC Clanton
Value Count Frequency (%)
of 28047
 
8.4%
chicago 8545
 
2.6%
auto 8253
 
2.5%
dodge 7557
 
2.3%
chrysler 7393
 
2.2%
jeep 7391
 
2.2%
ram 7389
 
2.2%
ford 6413
 
1.9%
chevrolet 6157
 
1.8%
motors 5387
 
1.6%
Other values (625) 241047
72.3%
2024-05-19T23:58:55.458146 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 2182255
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 2182255
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 2182255
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

dealer_zip
Real number (ℝ)

MISSING 

Distinct 285
Distinct (%) 0.3%
Missing 941
Missing (%) 1.0%
Infinite 0
Infinite (%) 0.0%
Mean 59746.335
Minimum 1060
Maximum 99301
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:55.744261 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 1060
5-th percentile 60004
Q1 60126
median 60445
Q3 60532
95-th percentile 60659
Maximum 99301
Range 98241
Interquartile range (IQR) 406

Descriptive statistics

Standard deviation 3239.2381
Coefficient of variation (CV) 0.054216516
Kurtosis 48.749856
Mean 59746.335
Median Absolute Deviation (MAD) 197
Skewness -4.7607426
Sum 5.4192913 × 109
Variance 10492664
Monotonicity Not monotonic
2024-05-19T23:58:55.909745 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
60540 5649
 
6.2%
60515 4478
 
4.9%
60525 3182
 
3.5%
60126 3180
 
3.5%
46322 2660
 
2.9%
60453 2643
 
2.9%
60477 2600
 
2.8%
60559 2436
 
2.7%
60035 2210
 
2.4%
60074 2115
 
2.3%
Other values (275) 59552
65.0%
Value Count Frequency (%)
1060 2
< 0.1%
1201 1
< 0.1%
2601 2
< 0.1%
2886 1
< 0.1%
3878 1
< 0.1%
4072 1
< 0.1%
4074 2
< 0.1%
5158 2
< 0.1%
6066 1
< 0.1%
6405 1
< 0.1%
Value Count Frequency (%)
99301 1
 
< 0.1%
99201 2
< 0.1%
99019 1
 
< 0.1%
98037 1
 
< 0.1%
98036 1
 
< 0.1%
97005 2
< 0.1%
95757 1
 
< 0.1%
95661 1
 
< 0.1%
95407 3
< 0.1%
95129 1
 
< 0.1%

mileage
Real number (ℝ)

MISSING  ZEROS 

Distinct 17169
Distinct (%) 19.2%
Missing 2366
Missing (%) 2.6%
Infinite 0
Infinite (%) 0.0%
Mean 30085.104
Minimum 0
Maximum 962839
Zeros 3630
Zeros (%) 4.0%
Negative 0
Negative (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:56.079457 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 1
Q1 7
median 3146.5
Q3 51176
95-th percentile 119510
Maximum 962839
Range 962839
Interquartile range (IQR) 51169

Descriptive statistics

Standard deviation 43318.389
Coefficient of variation (CV) 1.4398617
Kurtosis 5.6666895
Mean 30085.104
Median Absolute Deviation (MAD) 3146.5
Skewness 1.8043339
Sum 2.685998 × 109
Variance 1.8764829 × 109
Monotonicity Not monotonic
2024-05-19T23:58:56.252247 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
5 6126
 
6.7%
10 5627
 
6.1%
0 3630
 
4.0%
3 3280
 
3.6%
6 2984
 
3.3%
2 2263
 
2.5%
1 2141
 
2.3%
7 2093
 
2.3%
11 1908
 
2.1%
4 1745
 
1.9%
Other values (17159) 57483
62.7%
(Missing) 2366
 
2.6%
Value Count Frequency (%)
0 3630
4.0%
1 2141
 
2.3%
2 2263
 
2.5%
3 3280
3.6%
4 1745
 
1.9%
5 6126
6.7%
6 2984
3.3%
7 2093
 
2.3%
8 1376
 
1.5%
9 1329
 
1.5%
Value Count Frequency (%)
962839 1
 
< 0.1%
440911 2
 
< 0.1%
426586 2
 
< 0.1%
398677 2
 
< 0.1%
385223 2
 
< 0.1%
350017 5
< 0.1%
324349 3
< 0.1%
318260 3
< 0.1%
317568 2
 
< 0.1%
317508 7
< 0.1%

make
Text

Distinct 62
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:56.514297 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 13
Median length 10
Mean length 6.3136089
Min length 3

Characters and Unicode

Total characters 578617
Distinct characters 46
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Chevrolet
2nd row RAM
3rd row Mercedes-Benz
4th row Honda
5th row Chevrolet
Value Count Frequency (%)
ford 7775
 
8.4%
chevrolet 7763
 
8.3%
mercedes-benz 6184
 
6.6%
bmw 5661
 
6.1%
nissan 5527
 
5.9%
hyundai 5053
 
5.4%
jeep 4987
 
5.4%
volkswagen 4588
 
4.9%
subaru 3535
 
3.8%
audi 3275
 
3.5%
Other values (56) 38718
41.6%
2024-05-19T23:58:57.000149 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 578617
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 578617
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 578617
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

bodystyle
Categorical

Distinct 10
Distinct (%) < 0.1%
Missing 517
Missing (%) 0.6%
Memory size 716.1 KiB
SUV
50928 
Sedan
16807 
Pickup Truck
6833 
Coupe
 
4651
Cargo Van
 
3342
Other values (5)
8568 

Length

Max length 13
Median length 3
Mean length 4.9849993
Min length 3

Characters and Unicode

Total characters 454278
Distinct characters 28
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row SUV
2nd row Cargo Van
3rd row Cargo Van
4th row SUV
5th row SUV

Common Values

Value Count Frequency (%)
SUV 50928
55.6%
Sedan 16807
 
18.3%
Pickup Truck 6833
 
7.5%
Coupe 4651
 
5.1%
Cargo Van 3342
 
3.6%
Hatchback 3305
 
3.6%
Convertible 3242
 
3.5%
Wagon 1049
 
1.1%
Passenger Van 779
 
0.9%
Minivan 193
 
0.2%
(Missing) 517
 
0.6%

Length

2024-05-19T23:58:57.154663 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:57.290220 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
suv 50928
49.9%
sedan 16807
 
16.5%
pickup 6833
 
6.7%
truck 6833
 
6.7%
coupe 4651
 
4.6%
van 4121
 
4.0%
cargo 3342
 
3.3%
hatchback 3305
 
3.2%
convertible 3242
 
3.2%
wagon 1049
 
1.0%
Other values (2) 972
 
1.0%

Most occurring characters

Value Count Frequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 454278
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 454278
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 454278
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

cat
Categorical

MISSING 

Distinct 39
Distinct (%) < 0.1%
Missing 1301
Missing (%) 1.4%
Memory size 716.1 KiB
crossover_compact
16603 
luxurysuv_crossover
10984 
crossover_midsize
7416 
suv_midsize
5586 
luxurypassenger_standard
5272 
Other values (34)
44484 

Length

Max length 28
Median length 24
Mean length 16.751353
Min length 8

Characters and Unicode

Total characters 1513401
Distinct characters 25
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row ev_crossover_midsize
2nd row van_fullsize
3rd row van_fullsize
4th row crossover_compact
5th row crossover_midsize

Common Values

Value Count Frequency (%)
crossover_compact 16603
18.1%
luxurysuv_crossover 10984
12.0%
crossover_midsize 7416
 
8.1%
suv_midsize 5586
 
6.1%
luxurypassenger_standard 5272
 
5.8%
truck_fullsize 4542
 
5.0%
sedan_compact 4072
 
4.4%
van_fullsize 3918
 
4.3%
luxurypassenger_plus 3820
 
4.2%
sedan_midsize 3546
 
3.9%
Other values (29) 24586
26.8%

Length

2024-05-19T23:58:57.476038 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
crossover_compact 16603
18.4%
luxurysuv_crossover 10984
12.2%
crossover_midsize 7416
 
8.2%
suv_midsize 5586
 
6.2%
luxurypassenger_standard 5272
 
5.8%
truck_fullsize 4542
 
5.0%
sedan_compact 4072
 
4.5%
van_fullsize 3918
 
4.3%
luxurypassenger_plus 3820
 
4.2%
sedan_midsize 3546
 
3.9%
Other values (29) 24586
27.2%

Most occurring characters

Value Count Frequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 1513401
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1513401
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1513401
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

vin
Text

Distinct 40001
Distinct (%) 43.6%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:57.733166 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 17
Median length 17
Mean length 16.993704
Min length 7

Characters and Unicode

Total characters 1557405
Distinct characters 36
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 14282 ?
Unique (%) 15.6%

Sample

1st row 3GNKDCRJ6RS227894
2nd row 3C6LRVDG0RE118763
3rd row W1Y4KCHY8RT178723
4th row 5J6RS3H44RL004214
5th row 3GNAXHEG1RL299011
Value Count Frequency (%)
sajwa4ec0emb52401 14
 
< 0.1%
km8nu73c98u061498 14
 
< 0.1%
wdbwk56f46f111412 14
 
< 0.1%
1c3adebz8dv400466 14
 
< 0.1%
yv4952bl7b1101669 14
 
< 0.1%
wbsbf932xseh07679 14
 
< 0.1%
sbm16aea7pw001459 14
 
< 0.1%
yv1672mc5cj127325 14
 
< 0.1%
wvwhv71k37w053151 14
 
< 0.1%
wp1ae2a29dla14757 14
 
< 0.1%
Other values (39991) 91506
99.8%
2024-05-19T23:58:58.139863 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 1557405
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1557405
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1557405
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%
Distinct 4736
Distinct (%) 5.2%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
2024-05-19T23:58:58.562391 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 97
Median length 86
Mean length 23.129891
Min length 7

Characters and Unicode

Total characters 2119762
Distinct characters 80
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 464 ?
Unique (%) 0.5%

Sample

1st row Chevrolet:Blazer EV:RS
2nd row RAM:ProMaster 2500:High Roof
3rd row Mercedes-Benz:Sprinter 2500:High Roof
4th row Honda:CR-V:EX
5th row Chevrolet:Equinox:LS
Value Count Frequency (%)
4matic 2964
 
1.7%
premium 2711
 
1.5%
s 2686
 
1.5%
se 2582
 
1.5%
plus 2246
 
1.3%
jeep:grand 2198
 
1.2%
roof 1717
 
1.0%
package 1535
 
0.9%
xdrive 1462
 
0.8%
mercedes-benz:amg 1320
 
0.7%
Other values (4046) 155640
87.9%
2024-05-19T23:58:59.212619 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 2119762
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 2119762
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 2119762
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

fuel_type
Categorical

IMBALANCE 

Distinct 18
Distinct (%) < 0.1%
Missing 656
Missing (%) 0.7%
Memory size 716.1 KiB
Gasoline
78530 
Electric
 
4864
Hybrid
 
4188
Diesel
 
1930
E85 Flex Fuel
 
1347
Other values (13)
 
131

Length

Max length 29
Median length 8
Mean length 7.9477525
Min length 6

Characters and Unicode

Total characters 723166
Distinct characters 38
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) < 0.1%

Sample

1st row Electric
2nd row Gasoline
3rd row Diesel
4th row Gasoline
5th row Gasoline

Common Values

Value Count Frequency (%)
Gasoline 78530
85.7%
Electric 4864
 
5.3%
Hybrid 4188
 
4.6%
Diesel 1930
 
2.1%
E85 Flex Fuel 1347
 
1.5%
Plug-In Hybrid 33
 
< 0.1%
Flexible Fuel 21
 
< 0.1%
Bio Diesel 17
 
< 0.1%
Gasoline Fuel 16
 
< 0.1%
Electric with Ga 13
 
< 0.1%
Other values (8) 31
 
< 0.1%
(Missing) 656
 
0.7%

Length

2024-05-19T23:58:59.426932 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
gasoline 78546
83.7%
electric 4878
 
5.2%
hybrid 4224
 
4.5%
diesel 1947
 
2.1%
fuel 1386
 
1.5%
e85 1347
 
1.4%
flex 1347
 
1.4%
plug-in 38
 
< 0.1%
flexible 21
 
< 0.1%
bio 17
 
< 0.1%
Other values (12) 77
 
0.1%

Most occurring characters

Value Count Frequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 723166
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 723166
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 723166
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

stock_type
Categorical

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
Used
46230 
New
45416 

Length

Max length 4
Median length 4
Mean length 3.504441
Min length 3

Characters and Unicode

Total characters 321168
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row New
2nd row New
3rd row New
4th row New
5th row New

Common Values

Value Count Frequency (%)
Used 46230
50.4%
New 45416
49.6%

Length

2024-05-19T23:58:59.579728 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:59.682896 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
used 46230
50.4%
new 45416
49.6%

Most occurring characters

Value Count Frequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 321168
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 321168
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 321168
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

exterior_color
Text

MISSING 

Distinct 2511
Distinct (%) 2.8%
Missing 1042
Missing (%) 1.1%
Memory size 716.1 KiB
2024-05-19T23:58:59.940886 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Length

Max length 48
Median length 41
Mean length 14.797581
Min length 1

Characters and Unicode

Total characters 1340720
Distinct characters 47
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 317 ?
Unique (%) 0.3%

Sample

1st row sterling_gray_metallic
2nd row bright_white_clearcoat
3rd row blue_grey
4th row radiant_red_metallic
5th row summit_white
Value Count Frequency (%)
black 6103
 
6.7%
white 3580
 
4.0%
gray 2595
 
2.9%
summit_white 2372
 
2.6%
bright_white_clearcoat 2223
 
2.5%
silver 1591
 
1.8%
blue 1581
 
1.7%
red 1375
 
1.5%
oxford_white 1140
 
1.3%
black_sapphire_metallic 930
 
1.0%
Other values (2500) 67114
74.1%
2024-05-19T23:59:00.611536 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 1340720
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 1340720
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 1340720
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

page_channel
Categorical

CONSTANT 

Distinct 1
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 716.1 KiB
shopping
91646 

Length

Max length 8
Median length 8
Mean length 8
Min length 8

Characters and Unicode

Total characters 733168
Distinct characters 7
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row shopping
2nd row shopping
3rd row shopping
4th row shopping
5th row shopping

Common Values

Value Count Frequency (%)
shopping 91646
100.0%

Length

2024-05-19T23:59:00.771770 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:59:00.868864 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
shopping 91646
100.0%

Most occurring characters

Value Count Frequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 733168
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Interactions

2024-05-19T23:58:45.818867 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:41.778235 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.520632 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.340059 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.132985 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.028339 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.048235 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:41.909349 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.629390 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.474485 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.249823 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.131005 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.248676 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.019386 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.746016 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.586671 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.388545 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.290617 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.539829 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.130582 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.863708 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.696281 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.568187 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.402825 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.777033 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.265406 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.000341 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.902309 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.694231 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.562667 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.914548 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.406604 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.182208 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.020476 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.815932 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.697911 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-19T23:58:47.138203 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-19T23:58:47.658785 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0 msrp year canonical_mmty model local_zone interior_color aff_code price price_badge trim drivetrain dealer_name dealer_zip mileage make bodystyle cat vin canonical_mmt fuel_type stock_type exterior_color page_channel
0 0 57215.0 2024 Chevrolet:Blazer EV:RS:2024 Blazer EV NaN black national 54595.0 NaN RS All-wheel Drive Castle Rock Chevrolet GMC 80104.0 0.0 Chevrolet SUV ev_crossover_midsize 3GNKDCRJ6RS227894 Chevrolet:Blazer EV:RS Electric New sterling_gray_metallic shopping
1 1 58845.0 2024 RAM:ProMaster 2500:High Roof:2024 ProMaster 2500 NaN black national 52446.0 NaN High Roof Front-wheel Drive New Smyrna Chrysler Jeep Dodge RAM 32168.0 0.0 RAM Cargo Van van_fullsize 3C6LRVDG0RE118763 RAM:ProMaster 2500:High Roof Gasoline New bright_white_clearcoat shopping
2 2 58795.0 2024 Mercedes-Benz:Sprinter 2500:High Roof:2024 Sprinter 2500 NaN NaN national 54295.0 NaN High Roof Rear-wheel Drive Mercedes-Benz of Farmington 84025.0 8.0 Mercedes-Benz Cargo Van van_fullsize W1Y4KCHY8RT178723 Mercedes-Benz:Sprinter 2500:High Roof Diesel New blue_grey shopping
3 3 33815.0 2024 Honda:CR-V:EX:2024 CR-V NaN gray national NaN NaN EX Front-wheel Drive Kingman Honda 86409.0 7.0 Honda SUV crossover_compact 5J6RS3H44RL004214 Honda:CR-V:EX Gasoline New radiant_red_metallic shopping
4 4 27995.0 2024 Chevrolet:Equinox:LS:2024 Equinox NaN medium_ash_gray national 24803.0 NaN LS Front-wheel Drive McSweeney Chevrolet GMC Clanton 35045.0 0.0 Chevrolet SUV crossover_midsize 3GNAXHEG1RL299011 Chevrolet:Equinox:LS Gasoline New summit_white shopping
5 5 83630.0 2024 Audi:Q8 e-tron:Premium:2024 Q8 e-tron NaN pearl_beige national 83630.0 NaN Premium All-wheel Drive Audi Stuart 34997.0 20.0 Audi SUV ev_crossover_midsize WA15AAGE4RB021424 Audi:Q8 e-tron:Premium Electric New glacier_white_metallic shopping
6 6 33610.0 2024 Mitsubishi:Eclipse Cross:SEL:2024 Eclipse Cross NaN gray national 33610.0 NaN SEL Four-wheel Drive McClinton Auto Group 26101.0 5.0 Mitsubishi SUV crossover_compact JA4ATWAA2RZ046423 Mitsubishi:Eclipse Cross:SEL Gasoline New mercury_gray_metallic shopping
7 7 50185.0 2024 Dodge:Hornet:R/T Plus:2024 Hornet NaN black national 40185.0 NaN R/T Plus All-wheel Drive Don Jackson CDJR North 30028.0 16.0 Dodge SUV hybrid_suv ZACPDFDW9R3A24025 Dodge:Hornet:R/T Plus Hybrid New blue_steel shopping
8 8 27825.0 2024 Nissan:Kicks:SR:2024 Kicks NaN charcoal national 27825.0 NaN SR Front-wheel Drive Halladay Nissan 82001.0 6.0 Nissan SUV crossover_compact 3N1CP5DV6RL526633 Nissan:Kicks:SR Gasoline New scarlet_ember_tintcoat shopping
9 9 53727.0 2024 Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-Line:2024 Atlas Cross Sport NaN black_w/_blue_crust national 50727.0 NaN 2.0T SEL Premium R-Line All-wheel Drive AutoNation Volkswagen Las Vegas 89146.0 10.0 Volkswagen SUV suv_midsize 1V2FE2CA0RC238064 Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-Line Gasoline New platinum_gray_metallic shopping
Unnamed: 0 msrp year canonical_mmty model local_zone interior_color aff_code price price_badge trim drivetrain dealer_name dealer_zip mileage make bodystyle cat vin canonical_mmt fuel_type stock_type exterior_color page_channel
91636 91636 36669.0 2024 Subaru:Crosstrek:Wilderness:2024 Crosstrek NaN black national 36669.0 NaN Wilderness All-wheel Drive Gerald Subaru of Naperville 60540.0 6.0 Subaru SUV crossover_compact 4S4GUHU63R3780034 Subaru:Crosstrek:Wilderness Gasoline New sun_blaze_pearl shopping
91637 91637 51443.0 2024 Volkswagen:Atlas Cross Sport:2.0T SEL R-Line:2024 Atlas Cross Sport NaN mauro_brown national 47013.0 NaN 2.0T SEL R-Line All-wheel Drive Volkswagen of Downtown Chicago 60610.0 7.0 Volkswagen SUV suv_midsize 1V2AE2CA4RC213467 Volkswagen:Atlas Cross Sport:2.0T SEL R-Line Gasoline New platinum_gray_metallic shopping
91638 91638 NaN 2022 Jeep:Wagoneer:Series I:2022 Wagoneer NaN global_black national 49991.0 NaN Series I Four-wheel Drive Laurel BMW of Westmont 60559.0 17815.0 Jeep SUV suv_midsize 1C4SJVAT7NS227323 Jeep:Wagoneer:Series I Gasoline Used riverrock_green shopping
91639 91639 106555.0 2024 Lincoln:Navigator L:Reserve:2024 Navigator L NaN black_onyx national 104555.0 NaN Reserve Four-wheel Drive Fox Lincoln 60647.0 4.0 Lincoln SUV luxurysuv_suv 5LMJJ3LG7REL01031 Lincoln:Navigator L:Reserve Gasoline New silver_radiance_metallic shopping
91640 91640 34104.0 2024 Volkswagen:Taos:1.5T SE:2024 Taos NaN black national 31234.0 NaN 1.5T SE All-wheel Drive The Autobarn Volkswagen of Countryside 60525.0 6.0 Volkswagen SUV crossover_compact 3VVVX7B2XRM053891 Volkswagen:Taos:1.5T SE Gasoline New pure_white_/_black_roof shopping
91641 91641 57135.0 2024 RAM:1500:Tradesman:2024 1500 NaN black national 48135.0 NaN Tradesman Four-wheel Drive Zeigler Chrysler Dodge Jeep Ram of Schaumburg 60195.0 14.0 RAM Pickup Truck truck_fullsize 1C6SRFGTXRN165708 RAM:1500:Tradesman Gasoline New billet_silver_metallic_clearcoat shopping
91642 91642 0.0 2010 Volkswagen:Eos:Komfort:2010 Eos NaN titan_black national 11950.0 NaN Komfort Front-wheel Drive Net Motorcars 60101.0 86868.0 Volkswagen Convertible coupeconvertible_convertible WVWBA7AH7AV014923 Volkswagen:Eos:Komfort Gasoline Used reflex_silver_metallic shopping
91643 91643 83695.0 2024 RAM:1500:Longhorn:2024 1500 NaN mountain_brown national 73195.0 NaN Longhorn Four-wheel Drive Zeigler Chrysler Dodge Jeep Ram of Schaumburg 60195.0 15.0 RAM Pickup Truck truck_fullsize 1C6SRFKT9RN212671 RAM:1500:Longhorn Gasoline New diamond_black_crystal_pearlcoat shopping
91644 91644 75280.0 2024 Chevrolet:Suburban:LT:2024 Suburban NaN black national 71122.0 NaN LT Four-wheel Drive Phillips Chevrolet 60423.0 3.0 Chevrolet SUV suv_fullsize 1GNSKCKD4RR151802 Chevrolet:Suburban:LT Gasoline New black shopping
91645 91645 33010.0 2024 Ford:Escape:Active:2024 Escape NaN ebony_black national 31000.0 NaN Active All-wheel Drive Tasca Ford Midlothian 60445.0 12.0 Ford SUV crossover_compact 1FMCU9GN0RUA09502 Ford:Escape:Active Gasoline New black_metallic shopping